Real world problems with such properties are very common. For example in drug efficiency prediction problems, and more general
in chemical formulae property prediction problems, drugs/formulae are collections of molecules. Each molecule comes with its own
description, for example in terms of its physio-chemical properties, and/or its molecular structure. In language modeling, words are
features and the words' semantic and syntactic properties are their side-information. In image recognition, pixels are features
and their position is the side-information, and so on. Similar ideas also appear in tasks such as matrix completion, robust
PCA and collaborative filtering [1]}, [2]}, [3]}. There one seeks low rank
matrix decompositions in which the component matrices are constrained to follow relationships given by side-information
matrices, typically matrices which contain user and item descriptors.
